A practical breakdown of AI agents — how they plan, use tools, manage memory, and orchestrate multi-agent workflows to solve complex tasks autonomously.
A developer's guide to Azure AI Foundry — the model catalog, deployments, prompt engineering playground, agent framework, evaluation tools, and building production AI applications.
An introduction to the Model Context Protocol (MCP), how MCP servers work, and why they are a game-changer for AI-powered development workflows.
Practical architecture patterns for agent handoffs, contracts, retries, and safety checks in multi-agent systems.
A practical approach to continuously red team AI agents against injection, abuse, and data exfiltration risks.
Why context shape has become the primary determinant of quality in modern LLM products.
A practical framework for selecting the right large language model — covering use case mapping, cost vs capability tradeoffs, latency, context windows, and deployment constraints.